The present invention relates generally to obtaining data with respect to seaway conditions to which a submerged sea-going vessel is exposed under different operational conditions.
The estimation of seaway conditions including seastate, wave direction relative to a submerged sea-going vessel, its heading and the seaway induced suction force on the vessel surfaces, which increases exponentially as the vessel approaches the water surface, is of interest in order to offset the suction force by corrective vessel control so as to maintain keel depth and avoid broach. Attempts have been made to deal with the foregoing problem by development of control methods based on seastate estimation algorithms. However, none of such algorithm estimation methods is sufficiently robust for satisfactory use under actual seastate operation.
The use of a neural network type of signal processing system is generally known in the art as disclosed in U.S. Pat. No. 5,180,911 to Grossman et al. Use of such neural network in the signal processing system as disclosed in the Grossman et al. patent is associated with a complex input signal generating arrangement including optical waveguide, light beam launcher and photodetectors.
It is therefore an important object of the present invention to provide a digital signal processing system having a neural network architecture for seastate estimations based on input signals derived from sea-going vessel measurements so as to predict vessel surface suction force with computational simplicity and operational robustness.
In accordance with present invention, depth, pitch, roll and forward speed measurements of a sea-going vessel during underwater travel are respectively processed into signal inputs supplied to four neural networks arranged in accordance with an algorithmic architecture involving supply of variable outputs from two of the neural networks to three detectors through which four outputs from two of the neural networks are converted into accurate estimates of stem, beam and bow directional headings of the vessel. The variable output from a third neural network is supplied to a fourth detector through which wave direction relative to the vessel is estimated. The fourth neural network provides one output varied as a linear variable function supplied to a fifth detector for conversion into an estimate of sea energy at the sea surface.